CN102136123A - Target bank customer recognition system - Google Patents

Abstract

The invention provides a target bank customer recognition system, which comprises a basic data preprocessing device, a basic data storage device, a customer information measurement device, a customer characteristic extraction device and a target customer recognition device, wherein the basic data preprocessing device preprocesses basic customer recognition data; the basic data storage device stores the preprocessed basic customer recognition data; the customer information measurement device generates customer contribution data, customer importance data and customer value data; the customer characteristic extraction device acquires target customer sample data from the basic customer recognition data according to the customer contribution data, the customer importance data and the customer value data, and categories the target customer sample data according to the attribute of a customer to generate target customer characteristic data; and the target customer recognition device recognizes corresponding target customer information from the basic customer recognition data according to the target customer characteristic data, and outputting the target customer information. The system aims to solve the problems of financial customer information mining-based target customer recognition, customer information maintenance, information query and contribution measurement.

Description

A kind of target customer of bank recognition system

Technical field

The present invention is a kind of target customer of bank recognition system about Financial Information data mining treatment technology concretely.

Background technology

In the prior art, banking information system relates in the technology of Financial Information data mining processing in customer value measurement, contribution degree measurement and target customer's identification (or claiming core value client identification) etc., still rest on the stage of non-standard and non-quantification, this has just caused target customer's means of identification single, the drawback of resolution difference.

In order to solve above-mentioned drawback, the part banking information system is also arranged, set up the customer information storehouse by the mode of importing or manual typing in batches, and by special messenger's scheduled maintenance update customer information related data.The operation of these banking information system, realized to a certain extent based on functions such as the customer information maintenance that the personal finance customer information is excavated, information inquiry, contribution degree measurements, but this banking information system has only been optimized the customer information query function, and profound target customer discern processing capacity relatively a little less than, can't satisfy the needs of actual banking decision-making.

Summary of the invention

The embodiment of the invention provides a kind of target customer of bank recognition system, to solve the problem of target customer's identification, customer information maintenance, information inquiry and contribution degree measurement based on the financial client information excavating.

One of purpose of the present invention is, a kind of target customer of bank recognition system is provided, this system comprises: the basic data pretreatment unit, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and described client is discerned basic data carry out pre-service from bank network; Basic data storage device is used to store pretreated client and discerns basic data; The customer information measurement mechanism, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data; The client characteristics extraction element, be used for discerning basic data from the client and obtain target customer's sample data according to client's contribution degree data, client's importance degree data and customer value data, and according to client's attribute target customer's sample data is classified, generate target customer's characteristic; Target customer's recognition device is used for discerning basic data according to target customer's characteristic from the client and identifies corresponding target customer's information, and the export target customer information.

One of purpose of the present invention is, a kind of target customer of bank recognition system is provided, and this system comprises: basic data preprocessing server, basic data storage server, customer information are measured server, client characteristics extracts server, target customer's identified server and client's identification terminal; The basic data storage server is measured server with basic data preprocessing server, customer information respectively, client characteristics extracts server and target customer's identified server is connected; Client's identification terminal is connected with the basic data storage server by bank's internal network; The basic data preprocessing server, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and the client is discerned basic data carry out pre-service from bank network; The basic data storage server is used to store pretreated client and discerns basic data; Customer information is measured server, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data, and deposit the customer value data in the basic data storage server; Client characteristics extracts server, be used for discerning basic data from the client and obtain target customer's sample data according to client's contribution degree data, client's importance degree data and customer value data, and target customer's sample data is classified according to client's attribute, generate target customer's characteristic, and deposit target customer's characteristic in the basic data storage server; Target customer's identified server is used for discerning basic data according to target customer's characteristic from the client and identifies corresponding target customer's information, and deposits target customer's information in the basic data storage server; Client's identification terminal is used to import the target customer and discerns request, and the display-object customer information.

Beneficial effect of the present invention is, utilize three measurement models such as client's contribution degree DATA REASONING, client's importance degree DATA REASONING and customer value DATA REASONING, and based on the sorter of decision tree, from many aspects target customer's information is extracted, improved the accuracy of financial client data mining, extraction and relevant treatment.Improved the efficient that the target customer extracts.And then promoted bank individual client's service level.

Description of drawings

In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the structured flowchart of the embodiment of the invention 1 target customer of bank recognition system;

Fig. 2 is the structured flowchart of the embodiment of the invention 1 basic data pretreatment unit;

Fig. 3 discerns the basic database synoptic diagram for the embodiment of the invention 1 client;

Fig. 4 is the data scrubbing process flow diagram of the embodiment of the invention 1 basic data pretreatment unit;

Fig. 5 is the data integration process flow diagram of the embodiment of the invention 1 basic data pretreatment unit;

Fig. 6 is the data conversion process flow diagram of the embodiment of the invention 1 basic data pretreatment unit;

Fig. 7 a is the embodiment of the invention 1 a product contribution measuring unit structured flowchart;

Fig. 7 b is the embodiment of the invention 1 client's contribution degree measuring unit structured flowchart;

Fig. 8 is the structured flowchart of the embodiment of the invention 1 client's importance degree measuring unit;

Fig. 9 is the structured flowchart of the embodiment of the invention 1 customer information measurement mechanism;

Figure 10 is the embodiment of the invention 1 a decision tree synoptic diagram;

Figure 11 discerns processing flow chart for the embodiment of the invention 1 target customer;

Figure 12 is the embodiment of the invention 2 target customer of bank recognition system connection diagrams.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.

Embodiment 1

As shown in Figure 1, bank's target customer's recognition system of present embodiment comprises: basic data pretreatment unit 101, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and the client is discerned basic data carry out pre-service from bank network; Basic data storage device 102 is used to store pretreated client and discerns basic data; Customer information measurement mechanism 103, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data; Client characteristics extraction element 104, be used for discerning basic data from the client and obtain target customer's sample data according to client's contribution degree data, client's importance degree data and customer value data, and according to client's attribute target customer's sample data is classified, generate target customer's characteristic; Target customer's recognition device 105 is used for discerning basic data according to target customer's characteristic from the client and identifies corresponding target customer's information, and the export target customer information.Client's identification terminal 106 is used for the display-object customer information.

As shown in Figure 2, basic data pretreatment unit 101 comprises main control unit 10, data scrubbing unit 11, data integration unit 12 and data conversion unit 13.Main control unit 10 is responsible for the pretreated scheduling of data, it is according to user's requested service, visit basic data storage device 102, read the sample customer data to basic data pretreatment unit 101 from basic data storage device 102, the customer basis data are sent with charge free to data scrubbing unit 11, data integration unit 12 and data conversion unit 13 carry out pre-service, result is write back basic data storage device 102.

As shown in Figure 3, the client has discerned the basic data library storage and has comprised the various basic datas of client's essential information data, customer capital/asset-liabilities data, financial product configuration data and transaction details data.

Client's essential information data comprise at least: customer information number, account number and card number.Customer capital/asset-liabilities data comprise at least: the historical profit of client, the following profit function of client and rate of discount.

The financial product configuration data comprises at least: income is taken in, is deposited in consumption, the income of overdrawing, and hair fastener is paid, over-the-counter trading is paid, the ATM transaction is paid, the CDM transaction is paid, POS concludes the business expenditure, internet bank trade is paid, telephone bank's transaction is paid, mobile banking transaction is paid, the expenditure of concluding the business in batches.

The transaction details data comprise at least: the client is at the present fate R of the last distance of bank transaction, client total transaction count F in bank, the normalized value F of F ^*The client begins up till now time-to-live S, the normalized value S of S from time of initial purchase bank finance service ^*Client's income and cost ratio Normalized value Total service kind that bank that the client uses provides is counted T, the normalized value T of T ^*

Basic data pretreatment unit 101 is responsible for visit basic data storage device 102, randomly draw a collection of sample client, read this sample client's basic data, comprise client's essential information data, transaction data and the related data of adding up through simple analysis, and then carry out data scrubbing, integrated, conversion process, obtain sample client intermediate data, intermediate data is stored in the basic data storage device 102.

Data scrubbing unit 11 is responsible for adopting the data scrubbing routines, fills in vacancy data, smooth noise data, identification, deletion isolated point, and solve and inconsistent basic data is cleared up; Utilize recurrence or decision tree to conclude the method for determining and infer possible vacancy value; Adopt the method for branch mailbox, cluster, recurrence to eliminate noise.12 responsible data in a plurality of data sources are combined of data integration unit are done the consistance processing.The data conversion unit 13 responsible forms that data-switching is become to be suitable for excavating.Such as utilizing maximum-minimum specificationization that client properties is carried out linear transformation, property value is mapped in the corresponding interval.

As shown in Figure 4, the step of the data scrubbing unit of basic data pretreatment unit comprises:

Step 110: receive the customer data that main control unit 10 imports into, judge whether to carry out data scrubbing, if do not carry out, then execution in step 111; Otherwise, finish.

Step 111: customer data is carried out quality of data diagnosis; Quality of data diagnosis is that the basic condition of each table of raw data, each variable is checked and exploratory analysis, comprises data general condition diagnostic analysis, frequency analysis, univariate analysis, is respectively described below:

1) data general condition diagnostic analysis: whether exist repeated measures to diagnose in the major key of the variable number of the time attribute of each table, the granularity of data, data, the record number of data, data and the data.

2) frequency analysis: each variable of each tables of data (except major keys such as customer information number, account number, card number) is carried out the frequency analysis (FrequencyAnalysis) of classified variable (Categorical Variables), and output comprises: variate-value, variate-value definition, observation number, accounting, accumulation accounting.

3) univariate analysis: each variable of each tables of data (except major keys such as customer information number, account number, card number) is carried out the univariate analysis (Univariate Analysis) of continuous variable (Continuous Variables), and output comprises: the total number of records, non-missing values record number, missing values record number, average, standard deviation, minimum value, 1% fractile, 5% fractile, 10% fractile, 25% fractile, median, 75% fractile, 90% fractile, 95% fractile, 99% fractile, maximal value.

Step 112: according to data general condition diagnostic analysis result, duplicate record is screened to major key, rejects invalid data;

Step 113: the frequency analysis result at classified variable (Categorical Variables) is analyzed; Variate-value, variate-value are defined the record in the parameter list that does not drop on standard,, reject invalid data if inconsistent data then screens;

Step 114: the univariate analysis result at continuous variable (Continuous Variables) analyzes; To there being the record of " missing values record number ", if the critical field of analyzing is then given " default value " according to the mean value fill rule; Existence is departed from " average " surpasses 3 " standard deviation " above record, if the isolated point data then delete, if noise data then carries out smoothing processing;

Step 115: the customer data after will cleaning is passed to main control unit 10, and main control unit 10 writes back basic data storage device.

As shown in Figure 5, the treatment step of the data integration unit of basic data pretreatment unit comprises:

Step 120: receive the customer data that main control unit 10 imports into, judge whether to finish data scrubbing, if finished data scrubbing, then execution in step 121; Otherwise, finish.

Step 121: customer data is carried out the data relationship diagnosis; The data relationship diagnosis is that the incidence relation of each table of raw data, each variable is checked and exploratory analysis, comprises the alternate analysis of variable, is respectively described below:

The alternate analysis of variable:, utilize its internal logical relationship that some variablees of data are intersected diagnosis to there being the different variablees of logical relation in same the table; As utilize logical relation " day should equal to open an account the value date of fixed deposit " that " value date " in two phase savings schedules of adjusting, " opening an account day " are carried out alternate analysis.

Table 1

The value date and the date alternate analysis of opening an account

Table 1 is two alternate analysis results that adjust phase savings schedule variable V ALUEDAY (value date) and OPENDATE (opening an account day).

Step 122: the alternate analysis result at variable analyzes; Product category, Currency Type, Account Status, transaction channel, dealing money, account balance etc. are determined unified value rule;

Step 123: for type of credential is the client of I.D., considers that therefore 15 I.D.s and 18 I.D.s will transfer 15 ID (identity number) card No. to 18 ID (identity number) card No. corresponding to different customer information number;

Step 124: accomplish unified client's view for guaranteeing all business, determine the related major key of customer information conduct; Will " type of credential+certificate card number ", account number is all number unique corresponding with customer information;

Step 125: for the trans-regional nature person that causes who opens an account a plurality of customer information situations are arranged, need to integrate, comprehensively reflection client's condition of assets and in the preference of area, product and channel according to " type of credential+certificate card number ";

Step 126: the customer data after the data integration is passed to main control unit 10, and main control unit 10 writes back basic data storage device.

As shown in Figure 6, the treatment step of the data conversion unit of basic data pretreatment unit comprises:

Step 130: receive the customer data that main control unit 10 imports into, judge whether to finish data integration, if finished data integration, then execution in step 131; Otherwise, finish.

Step 131: the continuous variable (Continuous Variables) at different dimensions is carried out linear transformation; Relatively common method is " minimum-maximum specificationization ", supposes that promptly minA and maxA are respectively minimum and the maximal value of attribute A.Minimum-maximum specificationization is mapped to v ' in the interval [new_minA, new_maxA] by calculating value v with attribute A.Formula is as follows:

$v^{\′}=\frac{v-\min A}{\max A-\min A}(\mathrm{new}\_\mathrm{naxA}-\mathrm{new}\_\min A)+\mathrm{new}\_\min A$

Such as attribute income (income) is carried out maximum-minimum specificationization.Suppose minimum and value Fen Biewei $1000 and the $99000 of attribute income.We wish to shine upon income to interval [0.0,1.0].According to maximum-minimum specificationization, income value $25000 will be transformed to

Step 132: the customer data after the conversion is passed to main control unit 10, and main control unit 10 writes back basic data storage device.

Shown in Fig. 7 a, 7b, the customer information measurement mechanism further comprises: product contribution measuring unit, be used for the product income data and the product expenditure data of product configuration data are measured calculating, and generate the product contribution data; Client's contribution degree measuring unit is used for calculating generation client contribution degree data according to product contribution data and Measuring Time.

Product contribution measuring unit is measured calculating according to the contribution degree computation model of setting.The contribution degree score value that at first calculates each client and produced, the contribution of each bank product of holding according to the model parameter computing client, accumulative total is client's contribution, and client's contribution is carried out rank going entirely, determines contribution degree according to rank.Product contribution calculation formula is:

$K=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{R}_{{\mathrm{in}}_i}-\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{R}_{\mathrm{ou}{t}_j}---\left(1\right)$

In the formula (1): output valve is: product contribution K;

Input value is: R _InBe the income of product, i add up N channel for the channel of income, and channel includes but not limited to consumption, deposit, overdraws, settles accounts, other; R _OutBe the expenditure of product, j add up M channel for the channel of expenditure, and channel includes but not limited to hair fastener, changes card, over-the-counter trading, ATM transaction, CDM transaction, POS transaction, internet bank trade, telephone bank's transaction, mobile banking transaction, batch are concluded the business.

The total contribution calculation formula of client is:

$G=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{K}_i---\left(2\right)$

In the formula (2), output valve is: G is total contribution of client;

Input value is: K is the contribution of certain product in client's time period, and i is the product number, adds up N product, and product includes but not limited to bank card, (regular/current) savings, loan, fund, national debt, intermediary service, financing.

Client's contribution degree computing formula is:

$\mathrm{KPI}=\frac{\underset{t=T_0}{\overset{T_i}{\mathrm{\Σ}}}G\left(t\right)}{\left(\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{t=T_0}{\overset{T_t}{\mathrm{\Σ}}}g{\left(t\right)}_i\right)/N}---\left(3\right)$

In the formula (3), output valve is KPI, client's contribution degree;

Input value is: G (t) is total contribution of single client, and i is client's number, capable full N client, T ₀Be measurement period from date, T _tBe the measurement period cut-off date.

The contribution degree model data of formula (1) to (3) is write back basic data storage device.

As shown in Figure 8, the customer information measurement mechanism further comprises: client's importance degree measuring unit, be used for exchange hour, transaction count to the transaction details data, the time-to-live and the income of financial service measured calculating with the cost ratio, generates client's importance degree data.The method flow diagram of customer evaluation device importance degree measurement model.

Adopt the RFM method to carry out client's importance degree and measure calculating; The computing formula of RFM method is:

$I=\frac{1}{R}\×F^{*}\×S^{*}\×{\left(\frac{D}{A}\right)}^{*}\×T^{*}---\left(4\right)$

In formula (4), output valve is I, client's significance level;

Input value is: R is meant the client at the present fate of the last distance of bank transaction, and R is more little, and then the client just seems active more; F ^*Be meant the normalized value of F, and F is client's total transaction count in bank, obviously, F is big more, and the client is more loyal; S ^*Be meant the normalized value of S, S refers to the client and begins up till now time-to-live from time of initial purchase bank finance service, and S is big more, and the client also can be comparatively speaking than higher to the contribution degree of bank;

Be meant

Normalized value,

Be meant client's income and cost ratio, this index has reflected how many obtainable incomes of cost of per unit has, T ^*Be meant the normalized value of T, T is meant total service kind number that bank that the client uses provides, and T is big more, with regard to the client is described the service that bank provided is had higher satisfaction.

The importance degree measurement data that formula (4) is calculated writes back basic data storage device.

As shown in Figure 9, the customer information measurement mechanism further comprises: the customer value measuring unit, be used for calculating client's historical value data, client's current value data and client's potential value data respectively, and generate the customer value data according to client's historical value data, client's current value data and client's potential value data computation according to client's essential information data and customer capital/debt data.

Adopt the CV method that the client is carried out value assessment, customer value=historical value+current value+potential value is abbreviated as:

CV＝CHV+CCV+CPV (5)

In formula (5), CV:Customer Value client is to the aggregate value of enterprise (bank); CCV:Customer Current Value represents client's current value, and promptly the client is the profit value of bank's creation according to current buying behavior pattern in the future.

$\mathrm{CCV}=\underset{t=1}{\overset{N}{\mathrm{\Σ}}}{P}_0{\left(\frac{1}{1+d}\right)}^t---\left(6\right)$

P in formula (6) ₀Be the historical profit of the client of a nearest time quantum; D is a discount rate.

CHV:Customer Historic Value represents the historical profit value that the client has created for enterprise (bank); By being that every profit that bank creates is discounted in passing period, obtain the actual numerical value of CHV with the client.

$\mathrm{CHV}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{R}_{{\mathrm{in}}_i}{(1+d)}^t---\left(7\right)$

In formula (7), output valve is CHV, the product contribution;

Input value is: R _InBe the income of product, i add up N channel for the channel of income, and channel includes but not limited to consumption, deposit, overdraws, settles accounts, other; D is a discount rate.

CPV:Customer Potential Value represents client's potential value, and mensurable is following profit/income net present value (NPV).The client is from bringing the net present value (NPV) of profit now to bank between tailend up to its life cycle.Similarly, if when the cost management of certain bank can't reach accurate each client's of measurement profit situation, disposable income replaces the profit of formula in calculating.

$\mathrm{CPV}=\underset{t=1}{\overset{n}{\mathrm{\Σ}}}\mathrm{\π}\left(t\right)\×\frac{1}{{(1+d)}^t}---\left(8\right)$

In formula (8), π (t) is to be the following profits of customers function of independent variable with time t, and d is a discount rate, and n is the period of estimating that customer relationship from now on keeps.

The value assessment data are write back basic data storage device.

As shown in figure 10, the client characteristics extraction element further comprises: the decision tree generation unit, and be used for client's attribute is divided into a plurality of labels, and described target customer's sample data successively classified according to label, generate decision tree.

Decision tree provides a kind of displaying can obtain the method what is worth this rule-like under what conditions.It is a sorter, construct this sorter, needs a training sample data collection as input.Training set (Training set) is made of one group of data-base recording or tuple, each record is a proper vector of being made up of relevant field value, these fields are called attribute (Attribute), the attribute that is used to classify is called label (Label), and tag attributes is the classification mark of training set just.The form of a concrete sample be expressed as (V1, V2 ..., Vn; C), wherein Vi represents field value, and c represents classification.Training set is the basis of structural classification device.The type of tag attributes must disperse, and the number of the probable value of tag attributes few more good more (preferably two or three values).The number of label value is few more, and the error rate of the sorter that constructs is low more.

The number of each node child node of decision tree is relevant at the algorithm of usefulness with decision tree.Each branch or be a new decision node, or be the ending of tree, leaf be called.In the process that travels through from top to bottom along decision tree, all can run into a problem at each node, the difference of problem on each node is answered caused different branches, can arrive a leaf node at last.This process is exactly a process of utilizing decision tree to classify, utilizes several variablees (the corresponding problem of each variable) to judge affiliated classification (corresponding classification of each leaf meeting at last).

Set up the process of decision tree, promptly Shu growth course is the process of constantly data being carried out cutting, the corresponding problem of each cutting, also corresponding node." difference " maximum between the group that each cutting is all required to be divided into.The key distinction between the various decision Tree algorithms is exactly this " difference " to be weighed the difference of mode.

Present embodiment has adopted decision tree C5.0 algorithm to construct decision tree, but is not limited to C5.0, also can adopt other algorithms of decision tree.

For example, at first before client's contribution degree that the customer information measurement mechanism is counted, importance degree, the customer value overall ranking core value client of x% (x% takes from the parameter list of data storage device as sample data, be preset as 20%, can adjust according to the actual conditions of mechanism separately) by the user.According to the decision tree building method, client's base attribute is divided into a plurality of labels, successively classifies according to label, thereby constructs a decision tree.

Tag attributes:

Have in (1) 12 month one-man business loan and average every spot assets turnover 100,000 yuan and more than;

Average monthly income is more than 10,000 yuan in (2) 12 months,

Security class transaction count is more than or equal to 50 times in (3) 12 months;

In (4) 12 months the credit side settle accounts the class turnover surpass 10,000 yuan total transaction amount 300,000 yuan and more than.

According to the classification results of decision tree, arrangement core value client judges property set (i.e. path from the root node to the leafy node); As above shown in the example, the judgement property set of private owner is: have in 12 months one-man business loan and average every spot assets turnover 100,000 yuan and more than; Booming income crowd's judgement property set is: do not have the one-man business loan in 12 months, and in 12 months average monthly income more than 10,000 yuan; And the like.

This example is the example of having simplified, under the actual conditions, client properties more than these, comprising: client's essential information, comprehensive evaluation information, product use information, channel to use information, accounts information etc.For example client's sex, age, occupation, client's total assets, client's total liability, client's contribution degree, whether the blacklist client, whether the card holder, whether buy finance product, user of e-bank etc. whether.

Target customer's characteristic is write back basic data storage device.

As shown in figure 11, target customer's recognition device is used for discerning basic data according to target customer's characteristic from the client and identifies corresponding target customer's information, and the export target customer information.The treatment step of target customer's recognition device comprises:

Step 320: target customer's recognition device reads customer data to be identified and target customer's characteristic from basic data storage device;

Step 321:,, the client is segmented as age, educational background, individual total assets according to the principle of classification of target customer's feature;

Step 322: utilize target customer's characteristic, the client after the segmentation is discerned; For example to discern private owner, with regard to " have in 12 months one-man business loan and average every spot assets turnover 100,000 yuan and more than " attribute discern.

Step 323: client's recognition result data are write back basic data storage device.

Present embodiment utilizes three measurement models such as client's contribution degree DATA REASONING, client's importance degree DATA REASONING and customer value DATA REASONING, and based on the sorter of decision tree, from many aspects target customer's information is extracted, improved the accuracy of financial client data mining, extraction and relevant treatment.Improved the efficient that the target customer extracts.And then promoted bank individual client's service level.

Embodiment 2

As shown in figure 12, bank's target customer's recognition system of present embodiment comprises: basic data preprocessing server 201, basic data storage server 202, customer information are measured server 203, client characteristics extracts server 204, target customer's identified server 205 and client's identification terminal 206.

Basic data storage server 202 is measured server 203 with basic data preprocessing server 201, customer information respectively, client characteristics extracts server 204 and target customer's identified server 205 is connected; Client's identification terminal 206 is connected with basic data storage server 202 by bank's internal network.

Basic data preprocessing server 201, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and the client is discerned basic data carry out pre-service from bank network.

Basic data storage server 202 is used to store pretreated client and discerns basic data.

Customer information is measured server 203, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data, and deposit the customer value data in basic data storage server 202.

Client characteristics extracts server 204, be used for discerning basic data from described client and obtain target customer's sample data according to client's contribution degree data, client's importance degree data and customer value data, and target customer's sample data is classified according to client's attribute, generate target customer's characteristic, and deposit target customer's characteristic in basic data storage server 202.

Target customer's identified server 205 is used for discerning basic data according to target customer's characteristic from the client and identifies corresponding target customer's information, and deposits target customer's information in basic data storage server 202.

Client's identification terminal 206 is used to import the target customer and discerns request, and the display-object customer information.

Customer information is measured server 203 and is further comprised: product contribution measuring unit, be used for the product income data and the product expenditure data of product configuration data are measured calculating, and generate the product contribution data; Client's contribution degree measuring unit is used for calculating generation client contribution degree data according to product contribution data and Measuring Time.Client's importance degree measuring unit is used for exchange hour, the transaction count to the transaction details data, the time-to-live and the income of financial service measured calculating with the cost ratio, generates client's importance degree data.The customer value measuring unit, be used for calculating client's historical value data, client's current value data and client's potential value data respectively, and generate the customer value data according to described client's historical value data, client's current value data and client's potential value data computation according to client's essential information data and customer capital/debt data.

Client characteristics extracts server 204 and further comprises: the decision tree generation unit, and be used for client's attribute is divided into a plurality of labels, and target customer's sample data successively classified according to label, generate decision tree.

Present embodiment utilizes three measurement models such as client's contribution degree DATA REASONING, client's importance degree DATA REASONING and customer value DATA REASONING, and based on the sorter of decision tree, from many aspects target customer's information is extracted, improved the accuracy of financial client data mining, extraction and relevant treatment.Improved the efficient that the target customer extracts.And then promoted bank individual client's service level.

Used specific embodiment among the present invention principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.

Claims (10)

1. the target customer of bank recognition system is characterized in that described system comprises:

The basic data pretreatment unit, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and described client is discerned basic data carry out pre-service from bank network;

Basic data storage device is used to store pretreated client and discerns basic data;

The customer information measurement mechanism, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data;

The client characteristics extraction element, be used for discerning basic data from described client and obtain target customer's sample data according to described client's contribution degree data, client's importance degree data and customer value data, and according to client's attribute described target customer's sample data is classified, generate target customer's characteristic;

Target customer's recognition device is used for discerning basic data according to described target customer's characteristic from described client and identifies corresponding target customer's information, and exports described target customer's information.

2. system according to claim 1 is characterized in that, described customer information measurement mechanism further comprises:

Product contribution measuring unit is used for the product income data and the product expenditure data of described product configuration data are measured calculating, generates the product contribution data;

Client's contribution degree measuring unit is used for calculating the described client's contribution degree data of generation according to described product contribution data and Measuring Time.

3. system according to claim 1 is characterized in that, described customer information measurement mechanism further comprises:

Client's importance degree measuring unit is used for exchange hour, the transaction count to described transaction details data, the time-to-live and the income of financial service measured calculating with the cost ratio, generates described client's importance degree data.

4. system according to claim 1 is characterized in that, described customer information measurement mechanism further comprises:

The customer value measuring unit, be used for calculating client's historical value data, client's current value data and client's potential value data respectively, and generate described customer value data according to described client's historical value data, client's current value data and client's potential value data computation according to described client's essential information data and customer capital/debt data.

5. system according to claim 1 is characterized in that, described client characteristics extraction element further comprises:

The decision tree generation unit is used for described client's attribute is divided into a plurality of labels, and according to described label described target customer's sample data is successively classified, and generates described decision tree.

6. the target customer of bank recognition system, it is characterized in that described system comprises: basic data preprocessing server, basic data storage server, customer information are measured server, client characteristics extracts server, target customer's identified server and client's identification terminal;

Described basic data storage server is measured server with described basic data preprocessing server, customer information respectively, client characteristics extracts server and target customer's identified server is connected;

Described client's identification terminal is connected with described basic data storage server by bank's internal network;

Described basic data preprocessing server, be used for obtaining the client who comprises client's essential information data, customer capital/debt data, product configuration data and transaction details data and discern basic data, and described client is discerned basic data carry out pre-service from bank network;

Described basic data storage server is used to store pretreated client and discerns basic data;

Described customer information is measured server, be used for the product configuration data that reads is measured generation client contribution degree data, and the transaction details data that read are measured generate client's importance degree data, and client's essential information data of reading and customer capital/debt data are measured generate the customer value data, and deposit described customer value data in described basic data storage server;

Described client characteristics extracts server, be used for discerning basic data from described client and obtain target customer's sample data according to described client's contribution degree data, client's importance degree data and customer value data, and described target customer's sample data is classified according to client's attribute, generate target customer's characteristic, and deposit described target customer's characteristic in described basic data storage server;

Described target customer's identified server is used for discerning basic data according to described target customer's characteristic from described client and identifies corresponding target customer's information, and deposits described target customer's information in described basic data storage server;

Described client's identification terminal is used to import the target customer and discerns request, and shows described target customer's information.

7. system according to claim 6 is characterized in that, described customer information is measured server and further comprised:

Product contribution measuring unit is used for the product income data and the product expenditure data of described product configuration data are measured calculating, generates the product contribution data;

Client's contribution degree measuring unit is used for calculating the described client's contribution degree data of generation according to described product contribution data and Measuring Time.

8. system according to claim 6 is characterized in that, described customer information is measured server and further comprised:

Client's importance degree measuring unit is used for exchange hour, the transaction count to described transaction details data, the time-to-live and the income of financial service measured calculating with the cost ratio, generates described client's importance degree data.

9. system according to claim 6 is characterized in that, described customer information is measured server and further comprised:

The customer value measuring unit, be used for calculating client's historical value data, client's current value data and client's potential value data respectively, and generate described customer value data according to described client's historical value data, client's current value data and client's potential value data computation according to described client's essential information data and customer capital/debt data.

10. system according to claim 6 is characterized in that, described client characteristics extracts server and further comprises:

The decision tree generation unit is used for described client's attribute is divided into a plurality of labels, and according to described label described target customer's sample data is successively classified, and generates described decision tree.

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